Mini-project 5 - Getting started with {ggplot2}
Hazel Remolacio
5/18/2025
Data Source
For these projects we are using anonymized CDISC datasets, which can be found here: https://github.com/phuse-org/phuse-scripts/tree/master/data/adam/cdisc
In this mini-project we will be using the {ggplot2}
package to create data visualizations. {ggplot2} is our preferred
package for creating data visualizations as there are a large number of
ancillary packages that can be used to annotate and manipulate the
created plots.
In this mini-project we’ll be using the ggplot function
and defining how data columns map to plot aesthetics using the
aes function. We’re also going to be using various
functions that define how the data points are going to be displayed in
the plot - geom_point, geom_line. The
labs function allows you to annotate plots with titles,
subtitles, axes labels etc.
1. Create a dataset
This dataset is the Analysis Vital Signs from CDISC that was created
for training/testing purposes. We’re going to focus on the heart rate
measurement in this Mini Project. We are calculating a new variable
StudyWeek that defines the week within the Active Treatment
Period.
ADVS <- import("https://github.com/phuse-org/phuse-scripts/raw/master/data/adam/cdisc/advs.xpt")
PR <- ADVS %>%
filter(PARAMN == 3) %>%
filter(VISITNUM > 3) %>%
mutate(StudyWeek = floor(ADY/7))2. Use basic ggplot function
The basic ggplot command from the {ggplot2}
package creates a blank “canvas” on which to place points, lines, shapes
etc.
The data argument is the dataset used for the plot.
mapping identifies which columns in the data are used to
define aesthetics on the plot, which are passed to the
aes( ) function. So in this case the x axis is defined by
ADY which is the study day of the measurement. The y-axis
is the numeric value for the heart rate given in the column
AVAL. Note this this statement alone simply defines the
attributes of the plot - dataset and column mapping to plot
attributes.
While learning {ggplot2} we strongly recommend explicitly using
the mapping argument. This will
remind you that you are mapping between column values and attributes on
the plot. You will see many people skip naming this argument, but this
can lead to confusion and makes it harder to learn what is going on in
the {ggplot2} call.
ggplot(data = PR, mapping = aes(x=ADY, y=AVAL)) 
3. Add points to the “canvas” created in the previous step.
To make a scatterplot add ‘+ geom_point()’ to the above statement.
NOTE: always put the + sign at the end of a line, not at
the start.
NOTE: The {ggplot2} package was created
before the advent of the %>% operator. You can view the
+ operator within {ggplot2} as being analogous
to the %>% pipe. + is
adding additional layers to the plot. If you are
familiar with image editing software like Adobe Photoshop then you’ll
know that images within Photoshop can be made up of layers which are
edited individually.
By default, the geom_ functions will honour the column
to aesthetic attributes defined in the ggplot function
but it doesn’t have to. These geom_
functions have a mapping argument of their own to allow you
to redefine or change mappings if required.
ggplot(data = PR, mapping = aes(x=ADY, y=AVAL)) +
geom_point()## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
4. Identify groups of data
There is a group argument to the aes( )
function which allows you to define groupings / data series within the
dataset. In the plot above, it might be good to show which data
corresponds to different treatment arms.
ggplot(data = PR, mapping = aes(x=ADY, y=AVAL, group = TRTA)) +
geom_point()## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
Huh? That hasn’t done anything?
Well, yes it has. It’s just that you can’t see the change in the
graph. You’ve defined the grouping in the data that goes into the graph.
But did you want to see different colours or different shapes? If so,
then you need to map these to colour or shape attributes in the
aes( ) function.
ggplot(data = PR, mapping = aes(x=ADY, y=AVAL, group = TRTA,
colour = TRTA, shape = TRTA)) +
geom_point()## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
Mapping group attributes to column values in the
ggplot function i.e. changing attributes for every level of
a group variable means that subsequent geom_
settings are applied to each level of the group
variable.
5. Identify the data series from each subject to create a “Spaghetti plot”
This plot shows that while most subjects have ECG measurements on the same day, there are a few subjects that have ECGs at different times. What the plot does not show is how the points are grouped by individual i.e. each individual is measured multiple times, so how do we show which points are from the same subject?
Before running the chunk below, have a guess what the plot will look like.
ggplot(data = PR, mapping = aes(x=ADY, y=AVAL)) +
geom_line()
In Step 4 above, we defined a group mapping to show data
for each level of TRTA. We can use exactly the same
technique here to show the data for each
subject. Add a group attribute
within the aes function below to show the “spaghetti plot”
that shows the heart rate data series for each subject. Hint: patients
are identified using the USUBJID variable. Typically,
spaghetti plots use one colour for the lines linking observations from
the same subject so there is no need to specify a colour attribute
here.
plot1 <- PR %>%
ggplot(data = PR, mapping = aes(x=ADY, y=AVAL, group=USUBJID)) +
geom_line()
plot1## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
Note that we’re assigning the {ggplot2} plot to an
object called plot1. This is a good idea, since it means we
can pick up that object at any point and make modifications to it simply
by using the + operator and making changes.
ggplot2 plot objects can have any attribute changed or
added to using the + operator.
6. Add labels (titles, axes labels) and choose a theme for the plot
Change the text in the labs() statement below to make
sensible titles and axes labels for the plot.
plot2 <- plot1 +
labs(title = "Pulse Rates during Active Treatment Period",
subtitle = "Post-baseline",
x = "Analysis Relative Day",
y = "Pulse Rate (beats/min)",
caption = paste("Plot created on:",Sys.Date()))
plot2## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
You can also choose a theme for the plot from a wide range of
{ggplot2} themes. Other themes are available from a variety
of {ggplot2} helper packages such as
{cowplot}. Try out some different themes using
theme_... functions to see which one you like best.
library(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stampplot3 <- plot2 +
theme_bw()
plot3## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
plot3.2 <- plot2 +
theme_cowplot()
plot3.2## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
plot3.3 <- plot2 +
theme_light()
plot3.3## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
7. Split by treatment arm
The next thing we might want to do is to split the above plot (which
shows ALL subjects in the trial) by treatment arm. To do this, we want
to use a variable within the data to define which data goes in which
panel. We do this using the facet_ functions.
facet_grid splits the data by one or more specified
variables and arranges the plots in a grid on the page - one variable
defining the rows and (optionally) another variable specifying the
columns. facet_wrap takes one
variable and creates a new plot for each distinct value of that variable
and fills the page with as many plots as it can. Try out both here to
see which one works best in this situation:
plot4 <- plot3 +
facet_wrap(facets = "TRTA")
plot4## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
plot4.2 <- plot3 +
facet_grid(rows = vars(TRTA))
plot4.2## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
plot4.3 <- plot3 +
facet_grid(cols = vars(TRTA))
plot4.3## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
plot4.4 <- plot3 +
facet_grid(cols = vars(TRTA), rows = vars(SEX))
plot4.4## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
Now that you know how, you can employ ANY variable to split the plot e.g. SEX, PERIOD, …
8. Save the plot
Once you’re ready to save the plot, you can use the
ggsave function to render and externalise the plot in
whatever format you need. It’s a good idea to also save the final plot
object using saveRDS as you can then read this object into
R and make changes later.
NOTE: If you are saving from this .rmd file then the plots and output object will be saved wherever this .rmd file is located.
plot4 ## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
ggsave(filename = "myPlot.png", device = "png", width = 192, height = 108, units = "mm")## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).saveRDS(object = plot4, file = "savedPlot.rds")9. Add a summary statistic e.g. median
The spaghetti plot may be useful, but it would be good to show the median heart rate so we can get a feel for how this changes for each treatment arm.
We can do this using the built in {ggplot2} functions
stat_summary.
plot3 +
stat_summary(geom = "point", fun = median, color = "lightgreen")## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
In the plot3 object, we have a grouping attribute in the
ggplot function call mapped to USUBJID from
the dataset. By specifying this group aesthetic we are
telling R that any geom_ and stat_ functions
should use the same grouping. So that means that the
stat_summary is attempting to display the median values at
each time point for every subject. Which is
probably NOT what we actually want, since there is only one observation
per subject per time point.
So how do we go back to one showing medians calculate across ALL subjects?
plot3 +
stat_summary(mapping = aes(group = NULL), fn = median, colour = "lightgreen")## Warning in stat_summary(mapping = aes(group = NULL), fn = median, colour =
## "lightgreen"): Ignoring unknown parameters: `fn`## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_summary()`).## No summary function supplied, defaulting to `mean_se()`## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
Not all subjects have been assessed for vital signs on the same day
of the treatment period. Let’s look at the same plot as above, but using
the calculated variable StudyWeek.
ggplot(data = PR, mapping = aes(x=StudyWeek, y=AVAL)) +
geom_point()## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_point()`).
We can see that subjects have measurements on weeks between 0-30, and
then a few individuals having measurements at week 40. Let’s filter down
to the weeks 0, 5, 10, 15, 20, 25, 30 before applying the
stat_summary function:
dataWeeks <- PR %>%
filter(StudyWeek %in% c(0, 5, 10, 15, 20, 25, 30)) %>%
mutate(ADY = StudyWeek * 7)
dataWeeks %>%
ggplot(mapping = aes(x = StudyWeek, y = AVAL)) +
stat_summary(mapping = aes(group = NULL),
fun = median,
colour = "lightgreen")## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_segment()`).
Now we might want to superimpose these medians onto the spaghetti
plots above. But observe that the StudyWeek variable needs
to be rescaled to show “days” so that it can be plotted on the same
x-axis scale.
plot3 +
stat_summary(data = dataWeeks,
mapping = aes(x = ADY,
group = NULL),
fun = median,
colour = "lightgreen")## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_segment()`).
This plot illustrates how we can add a layer with new data (here, just the original data selecting the weeks we are interested in).
10. Axis bounds
Sometimes we want to limit the plot axes to “zoom in” on an area of
the plot where most of the data are. If you specify
scale_y_continuous(limits=c(50, 120)) then points outside
the range 50 - 120 are removed. If you specify
coord_cartesian(ylim=c(50, 120)) then you are effectively
zooming in to a particular range. Any values outside
that range will still exist and influence regression lines / smooths /
medians, but are not shown. Typically we will want to “zoom in” rather
than crop and eliminate observations outside of the range.
plot4 +
coord_cartesian(ylim=c(50, 120))## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
11. The ggplot2 object.
Let’s look at the plot4 object in more detail. You can
examine it using the Environment tab in RStudio IDE. Click on the
magnifying glass to the right of the object. plot4 is an R
list with type ggplot. Within it you’ll see it elements
such as the data, layers, scales, mapping, theme, coordinates, facet,
labels. This means that at any time you can extract
information from a ggplot object and use it.
To see the elements of a list in the RStudio IDE, go to the
Environment tab and then click on the blue arrow to the left of the
plot4 object. This will show a huge amount of information
about the plot - these are the plot attributes, which are contained in
an R list format. To access an element of a list, you can
use the <objectName>$<listItemName> syntax.
Let’s look at the mapping element of the plot4
object:
plot4$mapping## Aesthetic mapping:
## * `x` -> `ADY`
## * `y` -> `AVAL`
## * `group` -> `USUBJID`From this information, you’ll see that the x and
y attributes are being mapped to variable names
ADY and PARAM. What we might want to do is to
extract the variable names in these attributes
and use them to determine labels for the plot.
In programming, we sometimes want to use a variable name to tell us
which column contains values, and sometimes we just want to use the name
of that variable. In this case, because we want to just use the name, we
need to tell R to turn the variable “pointer” to a character string. We
do this using the quo_name function. We’ll look more at how
to refer to variables and use them in functions in a later
Mini-Project.
In the code below we have created a function called
uniqueVal. This function is going to be used to extract
information from dataset variables that can be used in axes labels.
Again, in a future Mini-Project we’ll look in more depth at creating
your own functions. For now, just be aware that we’ve written a function
to find the unique value of a given variable in the dataset. We have a
little defensive programming that checks that the variable doesn’t
contain more than one unique value.
By approaching the problem in this way, we avoid having to “hard code” axes labels and can more easily reuse code for different cases.
xLab <- quo_name(plot4$mapping$x)
yLab <- quo_name(plot4$mapping$y)
groupLab <- quo_name(plot4$mapping$group)
uniqueVal <- function(x){
if(length(unique(x))>1) simpleWarning(paste0("More than one value in column:",x))
unique(x)
}
study <- uniqueVal(plot4$data$STUDYID)
measure <- uniqueVal(plot4$data$PARAM)
xLab <- ifelse(xLab == "ADY", "Analysis Relative Day", xLab)
groupLab <- case_when(groupLab == "USUBJID" ~ "Subject",
groupLab == "TRTA" ~ "Treatment",
TRUE ~ groupLab)
plot4 +
labs(title = glue::glue("Study {study}"),
subtitle = glue::glue("Plot of {measure} by {groupLab}"),
x = xLab,
y = glue::glue("{measure}"))## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
12. Changing data
Now for the clever bit. {ggplot2} allows you to change
the dataset in a plot, and inheriting attributes from the previous plot.
You do this using the %+% command. On the left of the
%+% should be an object of type ggplot and on
the right should be a dataset object.
And if you use the code above to “guess” the labels for the plot from information within the data, then the labels automagically update themselves. Ideally, you might want to make the label guessing code into a function that takes the plot object as an input, but we’ll come to functions shortly.
SysBP <- ADVS %>%
filter(PARAMN == 1) %>%
filter(VISITNUM > 3)
SysBP_plot <- plot4 %+%
SysBP
xLab <- quo_name(SysBP_plot$mapping$x)
yLab <- quo_name(SysBP_plot$mapping$y)
groupLab <- quo_name(SysBP_plot$mapping$group)
study <- uniqueVal(SysBP_plot$data$STUDYID)
measure <- uniqueVal(SysBP_plot$data$PARAM)
xLab <- ifelse(xLab == "ADY", "Analysis Relative Day", xLab)
groupLab <- case_when(groupLab == "USUBJID" ~ "Subject",
groupLab == "TRTA" ~ "Treatment",
TRUE ~ groupLab)
SysBP_plot +
labs(title = glue::glue("Study {study}"),
subtitle = glue::glue("Plot of {measure} by {groupLab}"),
x = xLab,
y = glue::glue("{measure}"))## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
Challenge
Do the same for Diastolic BP and Temperature. Ensure that the axes
labels match the type of data used. Update plot4 for the
new endpoint.
create_plot <- function(plot_template, paramn_val){
param_data <- ADVS %>%
filter(PARAMN == {{paramn_val}}) %>%
filter(VISITNUM > 3)
param_plot <- {{plot_template}} %+%
param_data
xLab <- quo_name(param_plot$mapping$x)
yLab <- quo_name(param_plot$mapping$y)
groupLab <- quo_name(param_plot$mapping$group)
study <- uniqueVal(param_plot$data$STUDYID)
measure <- uniqueVal(param_plot$data$PARAM)
xLab <- ifelse(xLab == "ADY", "Analysis Relative Day", xLab)
groupLab <- case_when(groupLab == "USUBJID" ~ "Subject",
groupLab == "TRTA" ~ "Treatment",
TRUE ~ groupLab)
param_plot +
labs(title = glue::glue("Study {study}"),
subtitle = glue::glue("Plot of {measure} by {groupLab}"),
x = xLab,
y = glue::glue("{measure}"))
}
DiasBP_plot <- create_plot(plot4, 2)
Temp_plot <- create_plot(plot4, 6)
DiasBP_plot## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_line()`).
Temp_plot
Extra challenge
Read in vital signs data from another study which uses the
VS or ADVS dataset standards. What code needs
to change when you produce Pulse Rate plot for the new dataset (compared
to the Pulse Rate plot which uses data values to determine labels)?
create_plot <- function(data, plot_template, param, active_period){
param_data <- {{data}} %>%
filter(PARAM == {{param}}) %>%
filter(VISITNUM >= {{active_period}})
param_plot <- {{plot_template}} %+%
param_data
xLab <- quo_name(param_plot$mapping$x)
yLab <- quo_name(param_plot$mapping$y)
groupLab <- quo_name(param_plot$mapping$group)
study <- uniqueVal(param_plot$data$STUDYID)
measure <- uniqueVal(param_plot$data$PARAM)
xLab <- ifelse(xLab == "ADY", "Analysis Relative Day", xLab)
groupLab <- case_when(groupLab == "USUBJID" ~ "Subject",
groupLab == "TRTA" ~ "Treatment",
TRUE ~ groupLab)
param_plot +
labs(title = glue::glue("Study {study}"),
subtitle = glue::glue("Plot of {measure} by {groupLab}"),
x = xLab,
y = glue::glue("{measure}"))
}
ADVS_2 <- import("adam_advs_data_neurology.xpt")
PulseRate_plot <- create_plot(ADVS_2, plot4, "Heart Rate", 4)
PulseRate_plot
#Add as function inputs: dataset, start of active treatment period
#Filter PARAM instead of PARAMN since latter is not in the sample datasetsessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
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## * ── Packages attached to the search path.
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